Authors: F Mendivil R Shonkwiler
Publish Date: 2010/06/02
Volume: 147, Issue: 2, Pages: 395-410
Abstract
In this paper we adapt a genetic algorithm for constrained optimization problems We use a dynamic penalty approach along with some form of annealing thus forcing the search to concentrate on feasible solutions as the algorithm progresses We suggest two different generalpurpose methods for guaranteeing convergence to a globally optimal feasible solution neither of which makes any assumptions on the structure of the optimization problem The former involves modifying the GA evolution operators to yield a Boltzmanntype distribution on populations The latter incorporates a dynamic penalty along with a slow annealing of acceptance probabilities We prove that with probability one both of these methods will converge to a globally optimal feasible state
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